LAUSR

Objective This study aimed to comprehensively investigate the relationship between the survival differences and socioeconomic status (SES) in patients with multiple myeloma (MM) and construct a predictive nomogram to assess clinical outcomes of MM patients. Methods The Surveillance, Epidemiology, and End Results (SEER) census tract-level SES database provides two specialized attributes: SES index and rurality. Using this database, 37,819 patients diagnosed with MM between January 2007 and December 2016 were enrolled. We evaluated the effects of SES index on overall survival (OS) and myeloma-specific survival (MSS) using Kaplan-Meier curves and Cox regression analyses. Thereafter, we included 126 patients with MM from two independent medical centers in China and divided them into training (Center 1) and validation (Center 2) cohorts. Univariate and multivariate Cox analyses were used in the training cohort to construct a nomogram for predicting clinical outcomes. Nomogram performance was assessed using the area under the curve (AUC) and calibration curves. Results In the SEER cohort, lower SES was significantly associated with worse OS rates and MSS rates (both P < 0.001). Multivariate analysis confirmed SES as an independent predictor of survival. Subgroup analysis indicated an increasing linear trend in survival benefits in non-Hispanic White, married, insured, and urban populations with increasing SES (all P < 0.001). In the training cohort, albumin, creatinine, rurality, and SES were confirmed as independent prognostic indicators. A nomogram for OS prediction was developed using these four factors, and it showed satisfactory discrimination and calibration. The 18- and 36-month AUC values of the nomogram were 0.79 and 0.82, respectively. Based on the total nomogram points, patients were categorized into two risk levels with good separation. Conclusion SES strongly influences survival disparities in patients with MM. Our nomogram consisting of clinical and sociodemographic characteristics can potentially predict survival outcomes.